Affiliation:
1. ERZİNCAN BİNALİ YILDIRIM ÜNİVERSİTESİ, İKTİSADİ VE İDARİ BİLİMLER FAKÜLTESİ, BANKACILIK VE FİNANS BÖLÜMÜ
Abstract
Text mining; It is a multidisciplinary branch of knowledge that includes concepts and techniques from different fields such as information sciences, linguistics, computer science and data science. With the transition of organizations from paper data to electronic documents and digital records, the rapid digitization of business processes has increased the interest in text mining. Due to the growing data in the field of accounting, text mining technology has become an important research topic for this field. The aim of this study; In the field of accounting, by giving information on the use of text mining, it is to reveal the effect of this technology on organizations and individuals in the future in a concise way. As a result, the use of text mining technology in the field of accounting; accounting automation, audit automation, tax automation and business consultancy automation. In addition, it is predicted that text mining combined with artificial intelligence and machine learning approaches will offer significant opportunities to organizations and accounting professionals, as it automates processes much more.
Publisher
Muhasebe ve Vergi Uygulamalari Dergisi
Reference36 articles.
1. Alarcon, J., Fine T. & Ng, C. (2019). Accounting AI and machine learning: Applications and challenges. Accounting and Technology: PICPA’s Guide to an Evolving Profession, 3-7. Çevrimiçi http://onlinedigeditions.com /publication/?m= 14667&i=583202&p=0.
2. Aldhizer, G. R. (2017). Visual and text analytics. The CPA Journal, 87(6), 30-33. Çevrimiçi https://www.cpajournal.com/2017/06/20/visual-textanaly tics/.
3. Blake, C. (2011). Text mining. Annual Review of Information Science and Technology, 45(1), 121-155. http://doi.org/10.1002/aris.2011.1440450110.
4. Brown, B. & Rainey, S. (2018). Driving faster, more accurate and more beneficial tax decisions. IBM. Çevrimiçi https://www.ibm.com/blogs/watso n/2018/04/driving-faster-more-accurate-and-more-beneficial-tax-decisions/.
5. Chopra, S., Auli, M. & Rush, A. M. (2016). Abstractive sentence summarization with attentive recurrent neural networks. Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. San Diego, California, United States. Çevrimiçi https://www. aclweb.org/anthology/N16-1012/.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献